--- tags: - summarization - mT5 datasets: - csebuetnlp/xlsum language: - am - ar - az - bn - my - zh - en - fr - gu - ha - hi - ig - id - ja - rn - ko - ky - mr - ne - om - ps - fa - pcm - pt - pa - ru - gd - sr - si - so - es - sw - ta - te - th - ti - tr - uk - ur - uz - vi - cy - yo licenses: - cc-by-nc-sa-4.0 widget: - text: Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about vaccines in general". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. "We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO," the post said, referring to the World Health Organization. model-index: - name: csebuetnlp/mT5_multilingual_XLSum results: - task: type: summarization name: Summarization dataset: name: xsum type: xsum config: default split: test metrics: - name: ROUGE-1 type: rouge value: 36.5002 verified: true - name: ROUGE-2 type: rouge value: 13.934 verified: true - name: ROUGE-L type: rouge value: 28.9876 verified: true - name: ROUGE-LSUM type: rouge value: 28.9958 verified: true - name: loss type: loss value: 2.0674800872802734 verified: true - name: gen_len type: gen_len value: 26.9733 verified: true --- # mT5-multilingual-XLSum This repository contains the mT5 checkpoint finetuned on the 45 languages of [XL-Sum](https://huggingface.co./datasets/csebuetnlp/xlsum) dataset. For finetuning details and scripts, see the [paper](https://aclanthology.org/2021.findings-acl.413/) and the [official repository](https://github.com/csebuetnlp/xl-sum). ## Using this model in `transformers` (tested on 4.11.0.dev0) ```python import re from transformers import AutoTokenizer, AutoModelForSeq2SeqLM WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip())) article_text = """Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about vaccines in general". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. "We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO," the post said, referring to the World Health Organization.""" model_name = "csebuetnlp/mT5_multilingual_XLSum" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) input_ids = tokenizer( [WHITESPACE_HANDLER(article_text)], return_tensors="pt", padding="max_length", truncation=True, max_length=512 )["input_ids"] output_ids = model.generate( input_ids=input_ids, max_length=84, no_repeat_ngram_size=2, num_beams=4 )[0] summary = tokenizer.decode( output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(summary) ``` ## Benchmarks Scores on the XL-Sum test sets are as follows: Language | ROUGE-1 / ROUGE-2 / ROUGE-L ---------|---------------------------- Amharic | 20.0485 / 7.4111 / 18.0753 Arabic | 34.9107 / 14.7937 / 29.1623 Azerbaijani | 21.4227 / 9.5214 / 19.3331 Bengali | 29.5653 / 12.1095 / 25.1315 Burmese | 15.9626 / 5.1477 / 14.1819 Chinese (Simplified) | 39.4071 / 17.7913 / 33.406 Chinese (Traditional) | 37.1866 / 17.1432 / 31.6184 English | 37.601 / 15.1536 / 29.8817 French | 35.3398 / 16.1739 / 28.2041 Gujarati | 21.9619 / 7.7417 / 19.86 Hausa | 39.4375 / 17.6786 / 31.6667 Hindi | 38.5882 / 16.8802 / 32.0132 Igbo | 31.6148 / 10.1605 / 24.5309 Indonesian | 37.0049 / 17.0181 / 30.7561 Japanese | 48.1544 / 23.8482 / 37.3636 Kirundi | 31.9907 / 14.3685 / 25.8305 Korean | 23.6745 / 11.4478 / 22.3619 Kyrgyz | 18.3751 / 7.9608 / 16.5033 Marathi | 22.0141 / 9.5439 / 19.9208 Nepali | 26.6547 / 10.2479 / 24.2847 Oromo | 18.7025 / 6.1694 / 16.1862 Pashto | 38.4743 / 15.5475 / 31.9065 Persian | 36.9425 / 16.1934 / 30.0701 Pidgin | 37.9574 / 15.1234 / 29.872 Portuguese | 37.1676 / 15.9022 / 28.5586 Punjabi | 30.6973 / 12.2058 / 25.515 Russian | 32.2164 / 13.6386 / 26.1689 Scottish Gaelic | 29.0231 / 10.9893 / 22.8814 Serbian (Cyrillic) | 23.7841 / 7.9816 / 20.1379 Serbian (Latin) | 21.6443 / 6.6573 / 18.2336 Sinhala | 27.2901 / 13.3815 / 23.4699 Somali | 31.5563 / 11.5818 / 24.2232 Spanish | 31.5071 / 11.8767 / 24.0746 Swahili | 37.6673 / 17.8534 / 30.9146 Tamil | 24.3326 / 11.0553 / 22.0741 Telugu | 19.8571 / 7.0337 / 17.6101 Thai | 37.3951 / 17.275 / 28.8796 Tigrinya | 25.321 / 8.0157 / 21.1729 Turkish | 32.9304 / 15.5709 / 29.2622 Ukrainian | 23.9908 / 10.1431 / 20.9199 Urdu | 39.5579 / 18.3733 / 32.8442 Uzbek | 16.8281 / 6.3406 / 15.4055 Vietnamese | 32.8826 / 16.2247 / 26.0844 Welsh | 32.6599 / 11.596 / 26.1164 Yoruba | 31.6595 / 11.6599 / 25.0898 ## Citation If you use this model, please cite the following paper: ``` @inproceedings{hasan-etal-2021-xl, title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages", author = "Hasan, Tahmid and Bhattacharjee, Abhik and Islam, Md. Saiful and Mubasshir, Kazi and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M. Sohel and Shahriyar, Rifat", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.413", pages = "4693--4703", } ```